Accelerated Modeling of Near and Far-Field Diffraction for Coronagraphic Optical Systems

Accurately predicting the performance of coronagraphs and tolerancing optical surfaces for high-contrast imaging requires a detailed accounting of diffraction effects. Unlike simple Fraunhofer diffraction modeling, near and far-field diffraction effects, such as the Talbot effect, are captured by plane-to-plane propagation using Fresnel and angular spectrum propagation. This approach requires a sequence of computationally intensive Fourier transforms and quadratic phase functions, which limit the design and aberration sensitivity parameter space which can be explored at high-fidelity in the course of coronagraph design. This study presents the results of optimizing the multi-surface propagation module of the open source Physical Optics Propagation in PYthon (POPPY) package. This optimization was performed by implementing and benchmarking Fourier transforms and array operations on graphics processing units, as well as optimizing multithreaded numerical calculations using the NumExpr python library where appropriate, to speed the end-to-end simulation of observatory and coronagraph optical systems. Using realistic systems, this study demonstrates a greater than five-fold decrease in wall-clock runtime over POPPY's previous implementation and describes opportunities for further improvements in diffraction modeling performance.Comment: Presented at SPIE ASTI 2018, Austin Texas. 11 pages, 6 figure

Per Aspera ad Astra: On the Way to Parallel Processing

Computational Science and Engineering is being established as a third category of scientific methodology; this innovative discipline supports and supplements the traditional categories: theory and experiment, in order to solve the problems arising from complex systems challenging science and technology. While the successes of the past two decades in scientific computing have been achieved essentially by the technical breakthrough of the vector-supercomputers, today the discussion about the future of supercomputing is focussed on massively parallel computers. The discrepancy, however, between peak performance and sustained performance achievable with algorithmic kernels, software packages, and real applications is still disappointingly high. An important issue are programming models. While Message Passing on parallel computers with distributed memory is the only efficient programming paradigm available today, from a user's point of view it is hard to imagine that this programming model, rather than Shared Virtual Memory, will be capable to serve as the central basis in order to bring computing on massively parallel systems from a sheer computer science trend to the technological breakthrough needed to deal with the large applications of the future; this is especially true for commercial applications where explicit programming the data communication via Message Passing may turn out to be a huge software-technological barrier which nobody might be willing to surmount.KFA Jülich is one of the largest big-science research centres in Europe; its scientific and engineering activities are ranging from fundamental research to applied science and technology. KFA's Central Institute for Applied Mathematics (ZAM) is running the large-scale computing facilities and network systems at KFA and is providing communication services, general-purpose and supercomputer capacity also to the HLRZ ("Höchstleistungsrechenzentrum") established in 1987 in order to further enhance and promote computational science in Germany. Thus, at KFA - and in particular enforced by ZAM - supercomputing has received high priority since more than ten years. What particle accelerators mean to experimental physics, supercomputers mean to Computational Science and Engineering: Supercomputers are the accelerators of theory

Using Cognitive Computing for Learning Parallel Programming: An IBM Watson Solution

While modern parallel computing systems provide high performance resources, utilizing them to the highest extent requires advanced programming expertise. Programming for parallel computing systems is much more difficult than programming for sequential systems. OpenMP is an extension of C++ programming language that enables to express parallelism using compiler directives. While OpenMP alleviates parallel programming by reducing the lines of code that the programmer needs to write, deciding how and when to use these compiler directives is up to the programmer. Novice programmers may make mistakes that may lead to performance degradation or unexpected program behavior. Cognitive computing has shown impressive results in various domains, such as health or marketing. In this paper, we describe the use of IBM Watson cognitive system for education of novice parallel programmers. Using the dialogue service of the IBM Watson we have developed a solution that assists the programmer in avoiding common OpenMP mistakes. To evaluate our approach we have conducted a survey with a number of novice parallel programmers at the Linnaeus University, and obtained encouraging results with respect to usefulness of our approach

Parallelizing a network intrusion detection system using a GPU.

As network speeds continue to increase and attacks get increasingly more complicated, there is need to improved detection algorithms and improved performance of Network Intrusion Detection Systems (NIDS). Recently, several attempts have been made to use the underutilized parallel processing capabilities of GPUs, to offload the costly NIDS pattern matching algorithms. This thesis presents an interface for NIDS Snort that allows porting of the pattern-matching algorithm to run on a GPU. The analysis show that this system can achieve up to four times speedup over the existing Snort implementation and that GPUs can be effectively utilized to perform intensive computational processes like pattern matching

A parallel Fortran framework for neural networks and deep learning

This paper describes neural-fortran, a parallel Fortran framework for neural networks and deep learning. It features a simple interface to construct feed-forward neural networks of arbitrary structure and size, several activation functions, and stochastic gradient descent as the default optimization algorithm. Neural-fortran also leverages the Fortran 2018 standard collective subroutines to achieve data-based parallelism on shared- or distributed-memory machines. First, I describe the implementation of neural networks with Fortran derived types, whole-array arithmetic, and collective sum and broadcast operations to achieve parallelism. Second, I demonstrate the use of neural-fortran in an example of recognizing hand-written digits from images. Finally, I evaluate the computational performance in both serial and parallel modes. Ease of use and computational performance are similar to an existing popular machine learning framework, making neural-fortran a viable candidate for further development and use in production.Comment: Submitted to ACM SIGPLAN Fortran Forum. Reviewed by Arjen Markus and Izaak Beekma

Cross Teaching Parallelism and Ray Tracing: A Project-based Approach to Teaching Applied Parallel Computing

Massively parallel Graphics Processing Unit (GPU) hardware has become increasingly powerful, available and affordable. Software tools have also advanced to the point that programmers can write general purpose parallel programs that take advantage of the large number of compute cores available in the hardware. With literally hundreds of compute cores available on a single device, program performance can increase by orders of magnitude. We believe that introducing students to the concepts of parallel programming for massively parallel hardware is of increasing importance in an undergraduate computer science curriculum. Furthermore, we believe that students learn best when given projects that reflect real problems in computer science. This paper describes the experience of integrating two undergraduate computer science courses to enhance student learning in parallel computing concepts. In this cross teaching experience we structured the integration of the courses such that students studying parallel computing worked with students studying advanced rendering for approximately 30% of the quarter long courses. Working in teams on a joint project, both groups of students were able to see the application of parallelization to an existing software project with both the benefits and complications exposed early in the curriculum of both courses. Motivating projects and performance gains are discussed, as well as student survey data on the effectiveness of the learning outcomes. Both performance and survey data indicate a positive gain from the cross teaching experience

Integrated Development and Parallelization of Automated Dicentric Chromosome Identification Software to Expedite Biodosimetry Analysis

Manual cytogenetic biodosimetry lacks the ability to handle mass casualty events. We present an automated dicentric chromosome identification (ADCI) software utilizing parallel computing technology. A parallelization strategy combining data and task parallelism, as well as optimization of I/O operations, has been designed, implemented, and incorporated in ADCI. Experiments on an eight-core desktop show that our algorithm can expedite the process of ADCI by at least four folds. Experiments on Symmetric Computing, SHARCNET, Blue Gene/Q multi-processor computers demonstrate the capability of parallelized ADCI to process thousands of samples for cytogenetic biodosimetry in a few hours. This increase in speed underscores the effectiveness of parallelization in accelerating ADCI. Our software will be an important tool to handle the magnitude of mass casualty ionizing radiation events by expediting accurate detection of dicentric chromosomes